جستجوی مقالات مرتبط با کلیدواژه "بهره برداری مخزن" در نشریات گروه "آب و خاک"

One of the Principles of water resources management is the optimal use of the reservoirs as the main sources of surface water, and this issue has a special importance in the science of water engineering. In this research, the new K-means clustering method to discretize reservoir inflow has been presented for the Stochastic Dynamic Programming(SDP). In addition, the Moran's method is used to discretize the reservoir storage. By the programming in the Python environment, the historical reservoir inflow in each season is classified to different clusters and obtained the best inflow cluster for each season. The effects of this clustering is also considering in the SDP of Jamishan reservoir. In general, the change in inflow classification will lead to a fundamental change in the transition probability matrix. Thus, the use of K-means method for the reservoir inflow discretization, due to the possibility of optimizing the number of clusters in each time period, can be very useful for the SDP. finally, it is strongly recommended to use K-means method to discretize reservoir inflow for reservoir operation by SDP.

K-means is an object-based algorithm that selects representative clusters from the data itself rather than averaging them. K-means of a cluster is the most central element of a cluster. The purpose of this method is to reduce sensitivity to large values in the data set. Each cluster is introduced with one of the data close to the centers. According to the number of data categories (k), the value of the least squares function is minimized and the data are categorized in the best way. In addition, the Moran's method is used to discretize the reservoir storage. In this method, the upper and lower limit of the range of changes and the upper limit of each category are used as indicators of discretization of the reservoir volume. The study area includes Jamishan reservoir sub-basin with an area of 527.07 km2 located in the southwest of Sanghar city near the Pirsalman hydrometric station. This watershed is the part of Kermanshah province which is between 32-34° to 34-53° North latitude and 47-22° to 47-52° East longitude. The annual average of rainfall, evaporation and temperature are 441 mm, 1534 mm and 10 degrees Celsius, respectively.

Evaluating the performance of the K-means model in 4 different seasons, showed that among the 19 considered clusters, the best result in seasonalclassification is obtained by the 5 inflow clusters according to the performance rate in fall, winter, spring and summer seasons - 142.57, -176.90,-475.36 and -2.10, respectively. The results of the First-order Markov chain, the possible values are given in Table 1 for 4 seasons in 5 clusters,and in this table, the specified numbers indicate the probability of moving each cluster for each season.In thefollowing, using the backward recursive function, the calculations are continued until reaching the stationary state condition. Finally, the value of l* was obtained for all 4 periods and different combinations of k and i as Fig.1. The results of Steady-state condition showed that l* happened mostly in spring up to 5 clusters of the reservoir storage and the least happened in summer with onecluster. Then, the calculations of the reservoir release probability in each period for each class of inflow and storage have been made. The highest value has occurred for reservoir storage class 4 in the autumn, winter, and spring seasons but in the summer season, due to less inflow and high water demand, it has happened in reservoir storage class 5.

In this research, the Stochastic Dynamic Programming (SDP) of the Jamishan dam reservoir is discussed using the K-means method in classifying the inflow discharge seasonally for the 41 years of historical data. Moran's method is also used to classify the storage volume of the reservoir into 7 classes. To calculate the transition probability matrix during the first-order Markov chain process, it is necessary to have the flow class in each period. For this purpose, the k-means method is used. The reservoir inflow in each season is classified from 2 to 20 classes by programming in the Python environment and especially with the Scikit-learn library. Evaluating the performance of the K-means model in 4 different seasons, showed that among the 19 considered clusters, the best result in seasonal classification is obtained by the 5 inflow clusters. Changing the number of inflow clusters leads to changes in the transition probability matrix and this process would change the results of reservoir operation. It can be said that the use of this flow classification method can have a significant impact on the management and optimization of dam reservoir performance. In general, the use of new classification methods such as the K-means method in the discretization of reservoir inflow for the reservoir stochastic dynamic programming can be very beneficial and effective.

In operation of dam reservoir, due to the possibility of severe water shortages in the future, supplying total demand of current step is not rational, and the use of hedging rules can provide insurance for water supply in the future. In the reservoir long-term operation to supply the irrigation water demand, uncertainty of reservoir inflow and uncertainty of irrigation water demand have a significant effect on release. Crop water stress sensitivity variation at different growth stages varies the crop production function slope, which is not seen in seasonal production functions. In this study, a stochastic planning model with time-dependent production functions and a deterministic planning model with seasonal production function, in operation of the Buchan dam reservoir by using hedging rules are compared. The results show the reservoir operation by hedging rules increases economic benefit by 46.8% compared to the existing operation model. The time-dependent production function can improve the results by 19% over seasonal production functions. Also, the results show using stochastic model with the inflow uncertainty, irrigation water demand uncertainty and both, inflow uncertainty and irrigation water demand uncertainty simultaneously, the economic benefit increase by 0.73, 4.95 and 12.99%, respectively.

Predicting volume of water stored in reservoirs in the future periods plays an important role in planning and managing the optimal use of water resources systems. In this study, time series analysis method was used to predict the monthly inflow to Yamchi and Sabalan reservoirs in Ardabil province. The monthly flow data measured at hydrometric stations, located at the dam's entrance for 21 years (1994 to 2015) were used to build and test an appropriate model. The structures of the seasonal models were identified according to the auto-correlation charts (ACF) and partial auto-correlation (PACF), and then the appropriate model for each hydrometric station was selected based on the Akaike Information Criterion (AIC), Akaike Information Criterion Correction (AICC) and Bayesian Information Criterion (BIC). By fiting the model to the observational data, the parameters of each model were determined and the adequacy of the selected models was also examined by diagnostic tests. The results showed that ARIMA (1,0,0)(0,1,1)12 and ARIMA (1,1,1)(0,1,1)12 models, respectively for the monthly flow data of Yamchi and Arbabkandi stations have the lowest root mean square error (RMSE) and mean absolute error (MAE) and the highest determination coefficient. The values of these indicators in the model related to Yamchi hydrometric station were 1.04, 0.606 and 0.63, respectively, and for Arbabkandi hydrometric station were 1.35, 0.8 and 0.74, respectively. Therefore, the selected models accurately predict the monthly inflows to Yamchi and Sabalan reservoirs. Comparing the predicted results with the observational data showed that the selected models are not very accurate in predicting high discharge values.

Nowadays, water scarcity is the current issue in Iran. This issue made the more necessity of using the proper water resources management more than the past. Stochastic Dynamic programming (SDP) is one of the methods to obtain the reservoir operation rules. In this method, one of the most important factors to find the optimal solution is discretization of the storage capacity and reservoir inflow. In this research, some storage classes (3, 5, 7 and 10) are analyzed to achieve the optimum storage discretization by SDP method, considering tree types of objective function (α = 0, α = 0.5, α = 1) with the constant reservoir inflow classes.

In this study, the SDP model has been used to find the optimal storage of Jamishan reservoir by any objective functions. By using historical reservoir inflow time series, reservoir inflow and storage are discretized in 3 classes with equal length intervals method and also 3, 5, 7 and 10 classes by Moran method, respectively. This method is applied by driving objective function as a minimization of system damage for each composition of the reservoir inflow and storage classes (k, i). By achieving the steady policy at each period, the amount of reservoir Inflow, storage and release are deterministically defined.

The results showed that the optimal storage capacity, only water supply of downstream demands considered as an objective function, is k=7 and there is minimum water deficit in case of α=0. In addition, this would be 10 classes in case of α=1, which the amount of difference between reservoir storage and its desirable would be changed from constant value and the first decreasing change would be appear. Obtaining reservoir storage classes is also affected by method of discretization since this value is obtained 10 for classic and Moran method and 7 in Savarenskiy method. That is selected k = 10 based on the objective function in case of α = 0.5 considered two objectives of reservoir release storage volume simultaneously.

In case of α=0, the objective function is only reservoir release and water allocation, and of the optimal class of reservoir storage would occur at the point where water deficit is constant by increasing the number of storage classifications which k=7 is the optimal class. In the second scenario the objective function which is α=1 is selected as the best discretized class of the reservoir storage which has the closest vicinity to the target storage (Ts). So, in this case, k=10 is the optimum reservoir storage. In the third scenario, α = 0.5, there is a difference between to find the optimal solution when consider the TS or Tr as the criteria. Both objective function are well regarded in this case and also the first decreasing changes is happened in k=10.